Conversational recommender systems (CRS) dynamically obtain the user preferences via multi-turn questions and answers. The existing CRS solutions are widely dominated by deep reinforcement learning algorithms. However, deep reinforcement learning methods are often criticised for lacking interpretability and requiring a large amount of training data to perform. In this paper, we explore a simpler alternative and propose a decision tree based solution to CRS. The underlying challenge in CRS is that the same item can be described differently by different users. We show that decision trees are sufficient to characterize the interactions between users and items, and solve the key challenges in multi-turn CRS: namely which questions to ask, how to rank the candidate items, when to recommend, and how to handle negative feedback on the recommendations. Firstly, the training of decision trees enables us to find questions which effectively narrow down the search space. Secondly, by learning embeddings for each item and tree nodes, the candidate items can be ranked based on their similarity to the conversation context encoded by the tree nodes. Thirdly, the diversity of items associated with each tree node allows us to develop an early stopping strategy to decide when to make recommendations. Fourthly, when the user rejects a recommendation, we adaptively choose the next decision tree to improve subsequent questions and recommendations. Extensive experiments on three publicly available benchmark CRS datasets show that our approach provides significant improvement to the state of the art CRS methods.
翻译:通过多方向问答,动态地获得用户偏好。现有的CRS解决方案广泛以深强化学习算法为主。然而,深强化学习方法往往被批评为缺乏解释性,需要大量培训数据才能运行。在本文中,我们探索了更简单的替代方案,并向CRS提出了基于决策树的解决方案。CRS的根本挑战是,不同的用户可以对同一项目进行不同的描述。我们显示,决策树足以描述用户和项目之间的互动特点,并解决多方向 CRS的关键挑战:即询问的问题、如何排列候选项目、何时提出建议以及如何处理对建议提出的负面反馈。首先,对决策树的培训使我们能够找到有效缩小搜索空间的问题。第二,通过学习对每个项目和树节点的嵌入,候选项目可以根据其与树节点所编码的谈话环境的相似性进行排名。第三,与每个树节相关的项目的多样性使我们能够制定早期停止战略,以便决定何时对候选项目进行排名,何时对建议进行排名,如何对建议进行负面的反馈。第一,对决策树树树进行培训使我们在做出重大选择时,在选择调整后,用户提出调整时,如何改进我们提出选择了C标准。